Abstract 987: Seamless statistical analysis of adverse event data for clinical association
Authors: Dung-Tsa Chen, Jhanelle Gray, Andreas Saltos, Trevor A Rose, Alberto Chiappori, Zachary Thompson, Ram Thapa
Journal: Cancer Research (2023)

Abstract Purpose: Adverse event (AE) is a critical element in clinical trial to evaluate patient safety profile of the drug for benefit-risk assessment. AE is also shown to have clinical association. However, due to its complexity, utilization of AE data has been suboptimal. This study takes a unique approach to utilize AE parameters and to derive a set of innovative AE metrics which could have the potential as a predictive biomarker of treatment response and survival outcomes. Methods: The AE-derived biomarkers integrated toxicity severity level (grade) and treatment relatedness, with further tune-up by AE occurrence, frequency, and duration. Implementation of AE-derived biomarkers was performed in a comprehensive strategy starting from overall AE, toxicity category, down to individual AE. Landmark analysis at day 30 from initial treatment date was used to define early AE biomarkers. Two study cohorts (Cohort A and B) from two immunotherapy trials in late-stage non-small cell lung cancer were used to evaluate the potential of AE-derived biomarkers. Clinical outcomes for statistical analysis included progression-free survival, overall survival, disease control, and duration of treatment. Results: Both cohorts showed that early AEs were associated with clinical outcomes. Patients experienced with low-grade AEs (including treatment related AEs) at early time point had improved PFS, OS, and were associated with disease control. The significant early AEs included treatment related low-grade AE in overall AE, endocrine disorders, hypothyroidism (immune-related adverse event (irAE)), and platelet count decreased for Cohort A and low-grade AE in overall AE, gastrointestinal disorders, and nausea for Cohort B. In contrast, patients with early development of higher-grade AEs tended to have poorer PFS, OS, and correlated with PD. The associated early AEs included treatment related high-grade AE in overall AE, gastrointestinal disorders with two members, diarrhea and vomiting, for Cohort A and high-grade AE in overall AE, 3 toxicity categories, and 5 related individual AEs for Cohort B. One treatment related low-grade AE, alanine aminotransferase increased, was irAE and correlated with worse OS in Cohort A. Conclusions: The study demonstrated evidence of AE utility in predicting positive and negative clinical outcomes. It could be treatment related AEs or combination of treatment related and not related AEs. Grade level played a key role in determining direction of clinical outcomes with low-grade leaning to positive effect and high-grade to negative impact. Significant AEs covering overall AEs, toxicity category AEs, to individual AEs provided relatively comprehensive view of AE results for clinical relevance. Citation Format: Dung-Tsa Chen, Jhanelle Gray, Andreas Saltos, Trevor A Rose, Alberto Chiappori, Zachary Thompson, Ram Thapa. Seamless statistical analysis of adverse event data for clinical association [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 987.